Relevant bibliographies by topics / Anesthesia Data processing

Academic literature on the topic 'Anesthesia Data processing'

Author: Grafiati
Published: 10 December 2022
Last updated: 29 January 2023

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Journal articles on the topic "Anesthesia Data processing"

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Junger, A., L. Quinzio, A. Michel, G. Sciuk, C. Fuchs, K. Marquardt, G. Hempelmann, and M. Benson. "Data Processing at the Anesthesia Workstation: from Data Entry to Data Presentation." Methods of Information in Medicine 39, no. 04/05 (2000): 319–24. http://dx.doi.org/10.1055/s-0038-1634450.

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Abstract:Main requirements for an Anesthesia Information Management System (AIMS) are the supply of additional information for the anesthesiologist at his workstation and complete documentation of the anesthetic procedure. With the implementation of an AIMS (NarkoData) and effective user support, the quality of documentation and the information flow at the anesthesia workstation could be increased. Today, more than 20,000 anesthesia procedures are annually recorded with the AIMS at 112 decentralized workstations. The network for data entry and the presentation and evaluation of data, statistics and results directly available at the clinical workstation was made operational.
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MORI, T. "Data-processing for Quality Assurance of Anesthesia Care." JAPANES JOURNAL OF MEDICAL INSTRUMENTATION 63, no. 8 (August 1, 1993): 357–63. http://dx.doi.org/10.4286/ikakikaigaku.63.8_357.

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J. Brown, Michael, Arun Subramanian, Timothy B. Curry, Daryl J. Kor, Steven L. Moran, and Thomas R. Rohleder. "Improving operating room productivity via parallel anesthesia processing." International Journal of Health Care Quality Assurance 27, no. 8 (October 7, 2014): 697–706. http://dx.doi.org/10.1108/ijhcqa-11-2013-0129.

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Purpose – Parallel processing of regional anesthesia may improve operating room (OR) efficiency in patients undergoes upper extremity surgical procedures. The purpose of this paper is to evaluate whether performing regional anesthesia outside the OR in parallel increases total cases per day, improve efficiency and productivity. Design/methodology/approach – Data from all adult patients who underwent regional anesthesia as their primary anesthetic for upper extremity surgery over a one-year period were used to develop a simulation model. The model evaluated pure operating modes of regional anesthesia performed within and outside the OR in a parallel manner. The scenarios were used to evaluate how many surgeries could be completed in a standard work day (555 minutes) and assuming a standard three cases per day, what was the predicted end-of-day time overtime. Findings – Modeling results show that parallel processing of regional anesthesia increases the average cases per day for all surgeons included in the study. The average increase was 0.42 surgeries per day. Where it was assumed that three cases per day would be performed by all surgeons, the days going to overtime was reduced by 43 percent with parallel block. The overtime with parallel anesthesia was also projected to be 40 minutes less per day per surgeon. Research limitations/implications – Key limitations include the assumption that all cases used regional anesthesia in the comparisons. Many days may have both regional and general anesthesia. Also, as a case study, single-center research may limit generalizability. Practical implications – Perioperative care providers should consider parallel administration of regional anesthesia where there is a desire to increase daily upper extremity surgical case capacity. Where there are sufficient resources to do parallel anesthesia processing, efficiency and productivity can be significantly improved. Originality/value – Simulation modeling can be an effective tool to show practice change effects at a system-wide level.
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Fontanini, Alfredo, and James M. Bower. "Variable Coupling Between Olfactory System Activity and Respiration in Ketamine/Xylazine Anesthetized Rats." Journal of Neurophysiology 93, no. 6 (June 2005): 3573–81. http://dx.doi.org/10.1152/jn.01320.2004.

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In this study, we have characterized slow and fast oscillations at several stages of olfactory processing under light and deep ketamine/xylazine anesthesia in the albino rat. While monitoring the animal's respiration, we also obtained field potentials from the olfactory bulb and piriform (olfactory) cortex and simultaneously recorded membrane potentials in piriform cortex pyramidal cells. Our results demonstrate that oscillations are generally found at higher frequencies under lighter and lower frequencies under deeper anesthesia. In previous studies of cerebral cortex, similar results in ketamine/xylazine anesthetized animals have been interpreted to correspond with the higher frequencies found during waking and lower frequencies found in the sleep state. Correlation and coherence analysis between data obtained in the bulb and cortex reveals a clear difference in coupling depending on the anesthetic state of the animal. Specifically, activity recorded in the whole system is highly correlated with respiration during deep anesthesia, whereas only the olfactory bulb, and not the cortex, is correlated with respiration during light anesthesia. These data suggest that global activity in the piriform cortex is actually more directly tied to peripheral slow respiratory input during slow wave than fast wave states and that the coupling between olfactory structures can be dynamically modulated by the level of anesthesia and therefore presumably by different brain states as well.
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Lee, UnCheol, and George A. Mashour. "Role of Network Science in the Study of Anesthetic State Transitions." Anesthesiology 129, no. 5 (November 1, 2018): 1029–44. http://dx.doi.org/10.1097/aln.0000000000002228.

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Abstract The heterogeneity of molecular mechanisms, target neural circuits, and neurophysiologic effects of general anesthetics makes it difficult to develop a reliable and drug-invariant index of general anesthesia. No single brain region or mechanism has been identified as the neural correlate of consciousness, suggesting that consciousness might emerge through complex interactions of spatially and temporally distributed brain functions. The goal of this review article is to introduce the basic concepts of networks and explain why the application of network science to general anesthesia could be a pathway to discover a fundamental mechanism of anesthetic-induced unconsciousness. This article reviews data suggesting that reduced network efficiency, constrained network repertoires, and changes in cortical dynamics create inhospitable conditions for information processing and transfer, which lead to unconsciousness. This review proposes that network science is not just a useful tool but a necessary theoretical framework and method to uncover common principles of anesthetic-induced unconsciousness.
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Plourde, Gilles, Pascal Belin, Daniel Chartrand, Pierre Fiset, Steven B. Backman, Guoming Xie, and Robert J. Zatorre. "Cortical Processing of Complex Auditory Stimuli during Alterations of Consciousness with the General Anesthetic Propofol." Anesthesiology 104, no. 3 (March 1, 2006): 448–57. http://dx.doi.org/10.1097/00000542-200603000-00011.

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Background The extent to which complex auditory stimuli are processed and differentiated during general anesthesia is unknown. The authors used blood oxygenation level-dependent functional magnetic resonance imaging to examine the processing words (10 per period; compared with scrambled words) and nonspeech human vocal sounds (10 per period; compared with environmental sounds) during propofol anesthesia. Methods Seven healthy subjects were tested. Propofol was given by a computer-controlled pump to obtain stable plasma concentrations. Data were acquired during awake baseline, sedation (propofol concentration in arterial plasma: 0.64 +/- 0.13 microg/ml; mean +/- SD), general anesthesia (4.62 +/- 0.57 microg/ml), and recovery. Subjects were asked to memorize the words. Results During all periods including anesthesia, the sounds conditions combined elicited significantly greater activations than silence bilaterally in primary auditory cortices (Heschl gyrus) and adjacent regions within the planum temporale. During sedation and anesthesia, however, the magnitude of the activations was reduced by 40-50% (P < 0.05). Furthermore, anesthesia abolished voice-specific activations seen bilaterally in the superior temporal sulcus during the other periods as well as word-specific activations bilaterally in the Heschl gyrus, planum temporale, and superior temporal gyrus. However, scrambled words paradoxically elicited significantly more activation than normal words bilaterally in planum temporale during anesthesia. Recognition the next day occurred only for words presented during baseline plus recovery and was correlated (P < 0.01) with activity in right and left planum temporale. Conclusions The authors conclude that during anesthesia, the primary and association auditory cortices remain responsive to complex auditory stimuli, but in a nonspecific way such that the ability for higher-level analysis is lost.
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Ranft, Andreas, Daniel Golkowski, Tobias Kiel, Valentin Riedl, Philipp Kohl, Guido Rohrer, Joachim Pientka, et al. "Neural Correlates of Sevoflurane-induced Unconsciousness Identified by Simultaneous Functional Magnetic Resonance Imaging and Electroencephalography." Anesthesiology 125, no. 5 (November 1, 2016): 861–72. http://dx.doi.org/10.1097/aln.0000000000001322.

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Abstract Background The neural correlates of anesthetic-induced unconsciousness have yet to be fully elucidated. Sedative and anesthetic states induced by propofol have been studied extensively, consistently revealing a decrease of frontoparietal and thalamocortical connectivity. There is, however, less understanding of the effects of halogenated ethers on functional brain networks. Methods The authors recorded simultaneous resting-state functional magnetic resonance imaging and electroencephalography in 16 artificially ventilated volunteers during sevoflurane anesthesia at burst suppression and 3 and 2 vol% steady-state concentrations for 700 s each to assess functional connectivity changes compared to wakefulness. Electroencephalographic data were analyzed using symbolic transfer entropy (surrogate of information transfer) and permutation entropy (surrogate of cortical information processing). Functional magnetic resonance imaging data were analyzed by an independent component analysis and a region-of-interest–based analysis. Results Electroencephalographic analysis showed a significant reduction of anterior-to-posterior symbolic transfer entropy and global permutation entropy. At 2 vol% sevoflurane concentrations, frontal and thalamic networks identified by independent component analysis showed significantly reduced within-network connectivity. Primary sensory networks did not show a significant change. At burst suppression, all cortical networks showed significantly reduced functional connectivity. Region-of-interest–based thalamic connectivity at 2 vol% was significantly reduced to frontoparietal and posterior cingulate cortices but not to sensory areas. Conclusions Sevoflurane decreased frontal and thalamocortical connectivity. The changes in blood oxygenation level dependent connectivity were consistent with reduced anterior-to-posterior directed connectivity and reduced cortical information processing. These data advance the understanding of sevoflurane-induced unconsciousness and contribute to a neural basis of electroencephalographic measures that hold promise for intraoperative anesthesia monitoring.
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Pasma, Wietze, Linda M. Peelen, Stef van Buuren, Wilton A. van Klei, and Jurgen C. de Graaff. "Artifact Processing Methods Influence on Intraoperative Hypotension Quantification and Outcome Effect Estimates." Anesthesiology 132, no. 4 (April 1, 2020): 723–37. http://dx.doi.org/10.1097/aln.0000000000003131.

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Abstract Background Physiologic data that is automatically collected during anesthesia is widely used for medical record keeping and clinical research. These data contain artifacts, which are not relevant in clinical care, but may influence research results. The aim of this study was to explore the effect of different methods of filtering and processing artifacts in anesthesiology data on study findings in order to demonstrate the importance of proper artifact filtering. Methods The authors performed a systematic literature search to identify artifact filtering methods. Subsequently, these methods were applied to the data of anesthesia procedures with invasive blood pressure monitoring. Different hypotension measures were calculated (i.e., presence, duration, maximum deviation below threshold, and area under threshold) across different definitions (i.e., thresholds for mean arterial pressure of 50, 60, 65, 70 mmHg). These were then used to estimate the association with postoperative myocardial injury. Results After screening 3,585 papers, the authors included 38 papers that reported artifact filtering methods. The authors applied eight of these methods to the data of 2,988 anesthesia procedures. The occurrence of hypotension (defined with a threshold of 50 mmHg) varied from 24% with a median filter of seven measurements to 55% without an artifact filtering method, and between 76 and 90% with a threshold of 65 mmHg. Standardized odds ratios for presence of hypotension ranged from 1.16 (95% CI, 1.07 to 1.26) to 1.24 (1.14 to 1.34) when hypotension was defined with a threshold of 50 mmHg. Similar variations in standardized odds ratios were found when applying methods to other hypotension measures and definitions. Conclusions The method of artifact filtering can have substantial effects on estimates of hypotension prevalence. The effect on the association between intraoperative hypotension and postoperative myocardial injury was relatively small. Nevertheless, the authors recommend that researchers carefully consider artifacts handling and report the methodology used. Editor’s Perspective What We Already Know about This Topic What This Article Tells Us That Is New
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Darmayanti, Anita, Oka Yughana, and Bun Yurizali. "The Relationship of Risk Factors With The Incidence Of Postoperative Nausea And Vomiting In Patients who underwent surgery with General Anesthesia at Rsi Siti Rahmah." Science Midwifery 10, no. 4 (October 5, 2022): 3001–10. http://dx.doi.org/10.35335/midwifery.v10i4.739.

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General anesthesia is a medical procedure that aims to relieve pain, lose consciousness, and be predictable. General anesthesia has side effects in the form of PONV which cancause dehydration, electrolyte imbalance, re-open wounds, pulmonary aspiration and delay in discharge from the hospital. To know the characteristics of general anesthesia patients, to know the degree of PONV, and to know the relationship between patient, intraoperative, and postoperative risk factors who experience PONV in patients undergoing surgery under anesthesia general.The type of this research used is analytic observational with a cross-sectional approach. The affordable population in this study were all general anesthesia patients at Siti Rahmah Hospital Padang with 65 samples using a consecutive sampling technique. Analysis of univariate and bivariate data presented in the form of frequency and percentage distributions, data processing using the Kolgomorov Smirnov test statistical test. General anesthesia patients aged 26-35 years (24.6%), female (56.9%), no smoking history (89.2%), surgical oncology (47.7%), 60 minutes duration (67.7%), mild pain (38.5%), opioid use (92.3%). Grade 0 PONV (87.6%). The relationship of risk factors for patients experiencing PONV with age p = 0.288, gender p = 0.997, motion sickness p = 0.443, smoking history p = 0.958. Relationship of intraoperative risk factors for PONV with type of surgery p = 1,000, duration of surgery p = 0,978. The relationship between postoperative risk factors for experiencing PONV with opioid use p = 1,000, pain degree p = 1,000. Most general anesthesia patients were aged 26-35 years, most gender was female, most motion history was no history, most smoking history ie no history, the most type of surgery is oncology, the most duration is 60 minutes, the most opioid use is using, the highest degree of pain is mild.
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Mena Camejo, Dr Reynaldo, Yacnira Martínez Bazán, Dr C. Yurisnel Ortiz Sánchez, and Kenya Dalia Leon Paz. "Determination and calibration in the airway of the Mallampati, Patil-Aldreti tests, sternomentonian distance, interincisive distance." Journal of Anesthesia & Critical Care: Open Access 13, no. 1 (February 15, 2021): 47–53. http://dx.doi.org/10.15406/jaccoa.2021.13.00468.

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An analytical, cross-sectional study was carried out in patients undergoing major elective surgery under general orotracheal anesthesia, at the "Carlos Manuel de Céspedes" University Provincial Hospital of Bayamo, in the period from January to December 2018, with the objective of evaluating the effectiveness of predictive tests of difficult intubation: Mallampati, Patil-Aldreti test, sternomentonian distance, and interincisive distance. For the calculation of the sample, the professional statistical program Epidemiological Analysis of Tabulated Data was applied, resulting in 269 patients, who were selected in the anesthesia office and applied the tests, after signing the informed consent. For the information processing, descriptive and inferential statistics were used. According to the Youden index, the interincisive distance was unsurpassed in effectiveness in all the tests performed, following the sternomentonian distance; The Mallampati test was the one with the lowest predictive value. Combinations of tests can increase the diagnostic value compared to the value of each test alone.
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Dissertations / Theses on the topic "Anesthesia Data processing"

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Numanovic, Kerim. "Advanced Clinical Data Processing: A Predictive Maintenance Model for Anesthesia Machines." Thesis, KTH, Tillämpad fysik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-283323.

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The maintenance of medical devices is of great importance to ensure that the devices are stable, well-functioning, and safe to use. The current method of maintenance, which is called preventive maintenance, has its advantages but can be problematic both from an operators’ and a manufacturers’ side. Developing a model that will predict failure in anesthesia machines can be of great use for the manufacturer, the customers, and the patients. This thesis sets to examine the possibility of creating a predictive maintenance model for anesthesia machines by utilizing device data and machine learning. This thesis also investigates the influence of the data on the model performance and compare different lag sizes and future horizons to model performance. The time-series data collected came from 87 unique devices and a specific test was chosen to be the output variable of the model. A whole pipeline was created, which included pre-processing of the data, feature engineering, and model development. Feature extraction was done on the time series data, with the help of a library called tsfresh, which transformed time series characteristics into features that would enable supervised learning. Two models were developed: logistic regression and XGBoost. The logistic regression model acted as a baseline model and the result of its performance was as expected, quite poor. The XGBoost yielded an AUCPR score of 0.21 on the full dataset and 0.32 on a downsampled dataset. Although a quite low score, it was surprisingly high considering the extreme class imbalance that existed in the dataset. No clear pattern was found between the lag sizes and future horizons with the model performance. Something that could be seen was that the data imbalance had a great impact on the model performance, which was discovered when the downsampled dataset with less class imbalance yielded a higher AUCPR score.
Underhållet av medicintekniska produkter är mycket viktigt för att säkerställa att enheterna är stabila, välfungerande och säkra att använda. Den nuvarande underhållsmetoden, som kallas förebyggande underhåll, har sina fördelar men kan vara problematisk både från operatörens och tillverkarsidan. Att utveckla en modell som förutsäger fel i anestesimaskiner kan vara till stor nytta för tillverkaren, kunderna och patienterna. Denna avhandling syftar till att undersöka möjligheten att skapa en förutsägbar underhållsmodell för anestesimaskiner genom att använda enhetsdata och maskininlärning. Denna avhandling undersöker också påverkan av data på modellprestanda och jämför olika fördröjningsstorlekar och framtida horisonter med modellprestanda. Tidsseriedata som samlats in kom från 87 unika enheter och ett specifikt test valdes för att vara modellens outputvariabel. En hel pipeline skapades, som inkluderade förbehandling av data, funktionsteknik och modellutveckling. Funktionsextraktion gjordes på tidsseriedata med hjälp av ett bibliotek som heter tsfresh, som förvandlade tidsserieegenskaper till funktioner som skulle möjliggöra övervakat lärande. Två modeller utvecklades: logistisk regression och XGBoost. Den logistiska regressionsmodellen fungerade som en basmodell och resultatet av dess prestanda var som förväntat ganska dåligt. XGBoost gav en AUCPR-poäng på 0,21 på hela datamängden och 0,32 på en nedmonterad datamängd. Även om det var en ganska låg poäng, var det överraskande högt med tanke på den extrema klassobalansen som fanns i datasetet. Inget tydligt mönster hittades mellan fördröjningsstorlekarna och framtida horisonter med modellprestanda. Något som kunde ses var att dataobalansen hade stor inverkan på modellens prestanda, vilket upptäcktes när den nedprovade datamängden med mindre obalans i klassen gav en högre AUCPR-poäng.
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Hawkins, Kevin Michael. "Development of an automated anesthesia system for the stabilization of physiological parameters in rodents." Link to electronic thesis, 2003. http://www.wpi.edu/Pubs/ETD/Available/etd-0424103-105500/.

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Navabi, Mohammad Jafar 1963. "EVALUATION OF A SYSTEM FOR REAL-TIME MEASUREMENT OF ANESTHETIC UPTAKE." Thesis, The University of Arizona, 1986. http://hdl.handle.net/10150/276582.

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"Quantitative ultrasonography in regional anesthesia." Thesis, 2009. http://library.cuhk.edu.hk/record=b6075528.

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Li, Xiang.
Thesis (Ph.D.)--Chinese University of Hong Kong, 2009.
Includes bibliographical references (leaves 161-184).
Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web.
Abstract and appendix also in Chinese.
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Books on the topic "Anesthesia Data processing"

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Edmonds, Harvey L. Computerized EMG monitoring in anesthesia and intensive care. Weert, The Netherlands: MP, 1988.

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Automation in anesthesia, a relief?: A systematic approach to computers in patient monitoring. Berlin: Springer-Verlag, 1987.

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Ferreira, David A. Recent advances in BIS guided TCI anesthesia. Hauppauge, N.Y: Nova Science Publishers, 2010.

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International Symposium on Computing in Anesthesia and Intensive Care (18th 1998 Hamamatsu City, Japan). State-of-the-art technology in anesthesia and intensive care: Proceedings of the 18th International Symposium on Computing in Anesthesia and Intensive Care held in Hamamatsu City, Japan on 18-21 March 1998. Edited by Doi Matsuyuki, Ikeda Kazuyuki 1932-, and Kazama Tomiei. Amsterdam: Elsevier, 1998.

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Anesthesia Informatics Health Informatics. Springer, 2008.

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Oyama, Tsutomu, Kazuo Sato, Kazuyuki Ikeda, Matsuyuki Doi, and Tomiei Kazama. Computing and Monitoring in Anesthesia and Intensive Care: Recent Technological Advances. Springer London, Limited, 2012.

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1932-, Ikeda Kazuyuki, ed. Computing and monitoring in anesthesia and intensive care: Recent technological advances. Tokyo: Springer-Verlag, 1992.

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Simulation In Anesthesia. Saunders, 2006.

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Oyama, Tsutomu, Kazuo Sato, Kazuyuki Ikeda, Matsuyuki Doi, and Tomiei Kazama. Computing and Monitoring in Anesthesia and Intensive Care: Recent Technological Advances. Springer, 2012.

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S, Graenstein J., ed. The Automated anesthesia record and alarm systems. Boston: Butterworths, 1987.

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Book chapters on the topic "Anesthesia Data processing"

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Chuang, Kai-Hsiang, Frank Kober, and Min-Chi Ku. "Quantitative Analysis of Renal Perfusion by Arterial Spin Labeling." In Methods in Molecular Biology, 655–66. New York, NY: Springer US, 2021. http://dx.doi.org/10.1007/978-1-0716-0978-1_39.

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AbstractThe signal intensity differences measured by an arterial-spin-labelling (ASL) magnetic resonance imaging (MRI) experiment are proportional to the local perfusion, which can be quantified with kinetic modeling. Here we present a step-by-step tutorial for the data post-processing needed to calculate an ASL perfusion map. The process of developing an analysis software is described with the essential program code, which involves nonlinear fitting a tracer kinetic model to the ASL data. Key parameters for the quantification are the arterial transit time (ATT), which is the time the labeled blood takes to flow from the labeling area to the tissue, and the tissue T1. As ATT varies with vasculature, physiology, anesthesia and pathology, it is recommended to measure it using multiple delay times. The tutorial explains how to analyze ASL data with multiple delay times and a T1 map for quantification.This chapter is based upon work from the COST Action PARENCHIMA, a community-driven network funded by the European Cooperation in Science and Technology (COST) program of the European Union, which aims to improve the reproducibility and standardization of renal MRI biomarkers. This analysis protocol chapter is complemented by two separate chapters describing the basic concept and experimental procedure.
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Mortazavi, Nasim, Cecile Staquet, Audrey Vanhaudenhuyse, Andrea Soddu, Marie-Elisabeth Faymonville, and Vincent Bonhomme. "Functional MRI in Anesthesia and Resting-State Networks." In Functional MRI, edited by S. Kathleen Bandt and Dennis D. Spencer, 174–207. Oxford University Press, 2018. http://dx.doi.org/10.1093/med/9780190297763.003.0010.

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This chapter reviews current knowledge of the effects of hypnotic anesthetic agents on brain resting-state networks (RSNs) that sustain consciousness. Although full exploration of the networks under anesthesia is not yet available, current evidence indicates that anesthetic agents with hypnotic properties dose-dependently modulate RSN functioning. Each anesthetic agent has specific effects that are not uniform within a given network and probably correlate with the specific clinical features observed when one agent or another is used. Observations made on RSNs during anesthesia are supplementary arguments to link the networks with specific aspects of consciousness and connectedness to the environment and to confirm their physiological functions. The precise link between observations made on RSNs during anesthesia and known biochemical targets of anesthetic agents, or their effects on systems that regulate the sleep–wake cycle, is not established yet. PET studies using radiolabeled probes that specifically target a neurotransmission system offer insights into the links. New technological advances and modes of functional data analysis, such as Granger causality and dynamic causal modeling, will help in obtaining a more in-depth exploration of the complex interactions between brain regions, their modulation by anesthesia, and their role in information processing by the brain. Effects of hypnosis on RSNs also have been studied. The hypnotic state is useful for performing surgical procedures and explorations without general anesthesia. The hypnotic state is associated with specific changes in the activity of RSNs that confirm hypnosis as a specific brain state, different from normal wakeful consciousness and anesthetic states.
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